Enhancing spatial data analysis with generative AI: a novel approach for geospatial intelligence

Spatial data analysis plays a vital role in domains such as urban planning, environmental monitoring, and disaster management. However, traditional analytical methods often face challenges due to data sparsity, inconsistency, and the lack of labeled data. This study presents an approach that integrates Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to address these limitations through synthetic data augmentation and enhanced feature representation. Using OpenStreetMap and MODIS satellite imagery datasets, the proposed method achieved notable improvements. GANs improved classification accuracy by approximately 12%, while VAEs contributed to an 18% improvement in root mean square error (RMSE) in predictive modeling. These findings demonstrate the potential of Generative AI to advance geospatial intelligence and support real-world decision-making processes.